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Machine Learning Model Serving Patterns and Best Practices

You're reading from   Machine Learning Model Serving Patterns and Best Practices A definitive guide to deploying, monitoring, and providing accessibility to ML models in production

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803249902
Length 336 pages
Edition 1st Edition
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Author (1):
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Md Johirul Islam Md Johirul Islam
Author Profile Icon Md Johirul Islam
Md Johirul Islam
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Table of Contents (22) Chapters Close

Preface 1. Part 1:Introduction to Model Serving
2. Chapter 1: Introducing Model Serving FREE CHAPTER 3. Chapter 2: Introducing Model Serving Patterns 4. Part 2:Patterns and Best Practices of Model Serving
5. Chapter 3: Stateless Model Serving 6. Chapter 4: Continuous Model Evaluation 7. Chapter 5: Keyed Prediction 8. Chapter 6: Batch Model Serving 9. Chapter 7: Online Learning Model Serving 10. Chapter 8: Two-Phase Model Serving 11. Chapter 9: Pipeline Pattern Model Serving 12. Chapter 10: Ensemble Model Serving Pattern 13. Chapter 11: Business Logic Pattern 14. Part 3:Introduction to Tools for Model Serving
15. Chapter 12: Exploring TensorFlow Serving 16. Chapter 13: Using Ray Serve 17. Chapter 14: Using BentoML 18. Part 4:Exploring Cloud Solutions
19. Chapter 15: Serving ML Models using a Fully Managed AWS Sagemaker Cloud Solution 20. Index 21. Other Books You May Enjoy

Introducing the business logic pattern

In this section, we will introduce you to the business logic pattern of serving models.

When we bring our model to production, some business logic will be required. Business logic is any code that is not directly related to inference by the ML model. We have used some business logic throughout the book for various purposes, such as checking the threshold for updating models and checking the input type. Some examples of business logic are as follows:

  • Authenticating the user: This kind of business logic is used to check whether a user has permission to call the APIs. We can’t keep our APIs public in most cases because this might be very risky, as our critical information can be compromised. If the models are involved in critical business decisions, then the APIs may be restricted to only a few groups of people as well. We need to check the user credentials at the very beginning of the API call. The code snippet that performs authentication...
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